© Springer International Publishing Switzerland 2015
Leonard Berliner and Heinz U. Lemke (eds.)An Information Technology Framework for Predictive, Preventive and Personalised MedicineAdvances in Predictive, Preventive and Personalised Medicine810.1007/978-3-319-12166-6_11. Introduction
(1)
Weill Medical College of Cornell University, New York, USA
(2)
New York Methodist Hospital, Brooklyn, NY, USA
(3)
Technical University of Berlin, Los Angeles, Berlin, USA
(4)
University of Southern California, California, USA
Abstract
This book explores ways in which the requirements and interrelationships between Predictive, Preventive and Personalized Medicine (PPPM), clinical medical practice, and basic medical research could be best served by information technology (IT). To avoid the problems inherent in formulating IT solutions in isolation, a use-case was developed employing hepatocellular carcinoma (HCC). The subject matter was approached from four separate, but interrelated, tasks: (1) review of current understanding and clinical practices relating to HCC; (2) propose an IT system for dealing with the vast amount of information relating to HCC, including clinical decision support and research needs; (3) determine the ways in which a clinical liver cancer center can contribute to this IT approach; and, (4) examine the enhancements and impact that the first three tasks, and therefore PPPM, will have on the management of HCC. An IT System for Predictive, Preventive and Personalized Medicine (ITS-PM) for HCC is presented to provide a comprehensive system to provide unified access to general medical and patient-specific information for medical researchers and health care providers from different disciplines including hepatologists, gastroenterologists, medical and surgical oncologists, liver transplant teams, interventional radiologists, and radiation oncologists.
Keywords
Personalized medicineHepatocellular carcinomaInformation technologyInformation technology system for predictivePreventive and personalized medicine (ITS-PM)Model guided therapyTherapy imaging and model management system (TIMMS)Digital patient modelPatient-specific modelModel-based medical evidenceBayesian network1.1 Introduction
This book was initially conceived to seek solutions to issues and tasks relating to Information Technology (IT) and Predictive, Preventive and Personalized Medicine (PPPM) as identified in the 2012 European Association for Predictive, Preventive and Personalised Medicine (EPMA) White Paper [1]. As such, this book explores ways in which the requirements and interrelationships between PPPM, clinical medical practice, and basic medical research could be best served by information technology (IT). To avoid the problems inherent in formulating IT solutions in isolation, it was decided to develop a use-case, employing the clinical topic of hepatocellular carcinoma (HCC). The subject matter is therefore approached from the point of view of four separate, but interrelated, tasks: (1) review of current understanding and clinical practices relating to the diagnosis and management of patients with HCC; (2) propose an IT system for dealing with the vast amount of information relating to HCC, including clinical decision support and research needs; (3) determine the ways in which a clinical liver cancer center can utilize and contribute to this IT approach; and, (4) examine the enhancements and impact that the first three tasks, and therefore PPPM, will have on the management of HCC.
The thirteen chapters of this book will describe the initial efforts in designing and implementing a clinical liver cancer program that will be organized in such a way as to allow transition from traditional medical practice toward predictive, preventive, and personalized medicine, as the appropriate and specific information and communication technologies are developed. The combined chapters will address current technical and clinical material regarding assessment, management, and treatment of patients with HCC, within the context of Model-Guided Therapy (MGT), Patient-Specific Modeling (PSM) to create a Digital Patient Model (DPM) and PPPM. It is the intention of the authors to lay the groundwork for a clinical IT solution to assist institutions preparing similar treatment programs (whether they be for liver tumors or any other form of malignancy) while making the transition to MGT and Personalized Medicine. It is also hoped that this IT approach will facilitate the linkage between clinicians and researchers seeking to develop a practical means of identifying clinically useful biomarkers, and incorporating them into clinical applications.
This article has been divided into thirteen chapters, to optimally address the breadth and organization of the subject matter.
1.
Introduction
2.
The Digital Patient Model and Model Guided Therapy
3.
Hepatocellular Carcinoma and Patient Assessment
4.
Role of Imaging in Hepatocellular Carcinoma
5.
Personalized Chemotherapy for Hepatocellular Carcinoma
6.
Surgical Treatment for Hepatocellular Carcinoma
7.
Minimally Invasive Therapies for Hepatocellular Cancer
8.
Radiation Oncology in the Treatment of Hepatocellular Carcinoma
9.
Design of an IT System for Hepatocellular Carcinoma
10.
Outlook and Expert Recommendations for Predictive, Preventive and Personalized Medicine and Hepatocellular Carcinoma
The scope of the book will be primarily concerned with issues relating to primary liver cancer (hepatocellular carcinoma) so that the focus on personalized health care can be maintained. (In a comprehensive liver cancer program, the addition of patients with other forms of liver tumors will significantly add to the complexities of patient management.)
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